{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:OEOUKGZOBI6IZMHXQDPIRQN33L","short_pith_number":"pith:OEOUKGZO","schema_version":"1.0","canonical_sha256":"711d451b2e0a3c8cb0f780de88c1bbdacf3da730ac18290fe6539d022f699424","source":{"kind":"arxiv","id":"2111.12161","version":2},"attestation_state":"computed","paper":{"title":"Sensitivity Analysis of Individual Treatment Effects: A Robust Conformal Inference Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Emmanuel J. Cand\\`es, Ying Jin, Zhimei Ren","submitted_at":"2021-11-23T21:40:39Z","abstract_excerpt":"We propose a model-free framework for sensitivity analysis of individual treatment effects (ITEs), building upon ideas from conformal inference. For any unit, our procedure reports the $\\Gamma$-value, a number which quantifies the minimum strength of confounding needed to explain away the evidence for ITE. Our approach rests on the reliable predictive inference of counterfactuals and ITEs in situations where the training data is confounded. Under the marginal sensitivity model of Tan (2006), we characterize the shift between the distribution of the observations and that of the counterfactuals."},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2111.12161","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2021-11-23T21:40:39Z","cross_cats_sorted":[],"title_canon_sha256":"20d5513773f0c52df87776baf0d236f6ef8f0248e6b906428c95f9620ccb2603","abstract_canon_sha256":"f2f20edeaf37f1a9d49c771f7c026818f5b3e351dd2a6ad2f27f721761d91780"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:17:18.621179Z","signature_b64":"DzcCIPiIV2+cqBSCRkX2bV2HPG3lWmsLeHfCRlyhHuHvbZYLm0rE6SL5KhJ+8aAjj49l9kmEGryqF+yWhDpHAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"711d451b2e0a3c8cb0f780de88c1bbdacf3da730ac18290fe6539d022f699424","last_reissued_at":"2026-07-05T04:17:18.620705Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:17:18.620705Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sensitivity Analysis of Individual Treatment Effects: A Robust Conformal Inference Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Emmanuel J. Cand\\`es, Ying Jin, Zhimei Ren","submitted_at":"2021-11-23T21:40:39Z","abstract_excerpt":"We propose a model-free framework for sensitivity analysis of individual treatment effects (ITEs), building upon ideas from conformal inference. For any unit, our procedure reports the $\\Gamma$-value, a number which quantifies the minimum strength of confounding needed to explain away the evidence for ITE. Our approach rests on the reliable predictive inference of counterfactuals and ITEs in situations where the training data is confounded. Under the marginal sensitivity model of Tan (2006), we characterize the shift between the distribution of the observations and that of the counterfactuals."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.12161","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2111.12161/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2111.12161","created_at":"2026-07-05T04:17:18.620761+00:00"},{"alias_kind":"arxiv_version","alias_value":"2111.12161v2","created_at":"2026-07-05T04:17:18.620761+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.12161","created_at":"2026-07-05T04:17:18.620761+00:00"},{"alias_kind":"pith_short_12","alias_value":"OEOUKGZOBI6I","created_at":"2026-07-05T04:17:18.620761+00:00"},{"alias_kind":"pith_short_16","alias_value":"OEOUKGZOBI6IZMHX","created_at":"2026-07-05T04:17:18.620761+00:00"},{"alias_kind":"pith_short_8","alias_value":"OEOUKGZO","created_at":"2026-07-05T04:17:18.620761+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.11381","citing_title":"From Observational Data to Clinical Recommendations: A Causal Framework for Estimating Patient-level Treatment Effects and Learning Policies","ref_index":29,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OEOUKGZOBI6IZMHXQDPIRQN33L","json":"https://pith.science/pith/OEOUKGZOBI6IZMHXQDPIRQN33L.json","graph_json":"https://pith.science/api/pith-number/OEOUKGZOBI6IZMHXQDPIRQN33L/graph.json","events_json":"https://pith.science/api/pith-number/OEOUKGZOBI6IZMHXQDPIRQN33L/events.json","paper":"https://pith.science/paper/OEOUKGZO"},"agent_actions":{"view_html":"https://pith.science/pith/OEOUKGZOBI6IZMHXQDPIRQN33L","download_json":"https://pith.science/pith/OEOUKGZOBI6IZMHXQDPIRQN33L.json","view_paper":"https://pith.science/paper/OEOUKGZO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2111.12161&json=true","fetch_graph":"https://pith.science/api/pith-number/OEOUKGZOBI6IZMHXQDPIRQN33L/graph.json","fetch_events":"https://pith.science/api/pith-number/OEOUKGZOBI6IZMHXQDPIRQN33L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OEOUKGZOBI6IZMHXQDPIRQN33L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OEOUKGZOBI6IZMHXQDPIRQN33L/action/storage_attestation","attest_author":"https://pith.science/pith/OEOUKGZOBI6IZMHXQDPIRQN33L/action/author_attestation","sign_citation":"https://pith.science/pith/OEOUKGZOBI6IZMHXQDPIRQN33L/action/citation_signature","submit_replication":"https://pith.science/pith/OEOUKGZOBI6IZMHXQDPIRQN33L/action/replication_record"}},"created_at":"2026-07-05T04:17:18.620761+00:00","updated_at":"2026-07-05T04:17:18.620761+00:00"}